This invention relates generally to a method and device to calculate the driving range for a vehicle, and more particularly to calculating the driving range for such a vehicle that is operating in an electric-only (i.e., charge-depleting) mode of a propulsion system that is relying at least in part on a battery-based power supply.
Hybrid and electric vehicles provide an alternative to conventional means of vehicular motive power by either supplementing (in the case of hybrids) or completely replacing (in the case of electric vehicles) a vehicle's traditional internal combustion engine (ICE). One form of such alternative vehicle is known as an extended range electric vehicle (EREV). In one embodiment of the EREV, primary electric drive is achieved with a battery or related rechargeable energy storage system (RESS) that acts as a direct current (DC) voltage source to a motor, generator or transmission that in turn can be used to provide the energy needed to rotate one or more of the vehicle's wheels. Once the electrical charge from the RESS has been depleted, backup power may come from an ICE to provide auxiliary onboard electrical energy generation. The Chevrolet Volt is an EREV that is being manufactured by the Assignee of the present invention.
Motive or related power to an EREV may be supplied by various battery architectures including nickel-metal hydride batteries, lead acid batteries, lithium polymer batteries and lithium-ion batteries. Of these, the lithium-ion battery appears to be particularly promising for RESS vehicular applications. Irrespective of the battery form, it would be inconvenient for a vehicular operator to correlate driving distance with available battery charge. Methods to perform this correlation are available, but they require detailed information about (among other things) a particular operator's driving behavior and geographic location, thereby making such a system impractical. For example, modeling simulation tools allow for output of vehicle energy-related metrics (including acceleration, fuel economy, driving distance or the like), but involve complex methodologies that require extensive training of engineers, researchers, computer programmers or related highly-skilled personnel in order to use such models successfully. Moreover, such models require large amounts of input data in order to be run correctly. Examples of such input data includes typical drive cycle (such as city, highway, aggressive or the like), hilliness (such as flat road, varying degrees of hill grades or the like), ambient and battery temperatures, tire pressures and type, cargo or passenger weights and multiple vehicle set-ups (battery size, engine size, transmission type/calibration, aerodynamics, accessory electric loads, air conditioning/heating settings or the like). Furthermore, such models also require high-powered computers to run the complex software.